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When something goes wrong in Formula 1, engineers don’t have time to search through thousands of files. Decisions have to happen at race pace. AI can investigate problems at speed, but only if test data is linked to the right build, software version and conditions. Watch Tomáš Neubauer, Quix CTO and co-founder, demonstrate how to give AI this context, so engineers can ask questions in plain language and get answers from their test data.

AI performs well on isolated engineering tasks but struggles with deep analysis across complex test data. This whitepaper sets out how to structure engineering data with the right context, metadata and relationships, so that AI agents can investigate engineering problems more effectively.

The engineering jobs to be done don’t change when the stakes are high. But the pressure to deliver does. At the forefront of F1, engineers strive to get insights and decisions faster than the competition. Teams up and down the grid are turning to AI agents that automate workflows, build dashboards, and analyse runs without specialist support. In this webinar Javier Blanco Cordero and Michael Rosam demonstrate how F1 engineers use AI to: ❇️ Trust the data before any analysis begins, knowing which channels are reliable and what is missing. ❇️ Diagnose whether an anomaly is a sensor, a worn component, or real system behaviour. ❇️ Trace performance across runs from months ago to now, without spending hours opening files one by one. ❇️ Know what serial number, build, and software version was running when an anomaly appeared. Watch the webinar on demand.

What could your team achieve with less time spent preparing data and more time solving engineering problems?

When something goes wrong in Formula 1, engineers do not have the time to go searching through thousands of files and channels. Decisions have to happen at race pace. AI can cut through the noise to identify patterns and anomalies at speed. But simply asking AI to compare a suspect run against every similar run from the last six months will fail. Recordings aren’t linked to the build, the software version, or the conditions they came from. So how are F1 organisations structuring data so that an engineer can ask a question in plain language and get an answer back? On 29th September, 2pm BST / 3pm CET, Michael Rosam and Tomáš Neubauer will demonstrate how to structure engineering data with the right context, metadata and relationships, so that AI agents can investigate engineering problems more effectively. Register now to find out how to: ❇️ Get thousands of channels from multiple vehicles into one searchable place ❇️ Keep the build, software version and conditions attached to every recording ❇️ Compare a new run against months of history without opening files one at a time ❇️ Let AI agents write the analysis to fit the question being asked ❇️ Judge whether an AI answer can be trusted before acting on it Save your seat 👇

During a test drive, a tyre's temperature spikes unexpectedly. Why? AI can find the answer in the telemetry but it needs a search engine. Suppose you want to ask an AI agent to “𝘧𝘪𝘯𝘥 𝘴𝘪𝘮𝘪𝘭𝘢𝘳 𝘤𝘢𝘴𝘦𝘴 𝘧𝘳𝘰𝘮 𝘵𝘩𝘦 𝘭𝘢𝘴𝘵 𝘴𝘪𝘹 𝘮𝘰𝘯𝘵𝘩𝘴 𝘢𝘯𝘥 𝘵𝘦𝘭𝘭 𝘮𝘦 𝘸𝘩𝘢𝘵 𝘵𝘩𝘦𝘺 𝘩𝘢𝘷𝘦 𝘪𝘯 𝘤𝘰𝘮𝘮𝘰𝘯” This only works if the underlying test data is connected to a clean ontology and semantic layer. In other words, your AI agent needs a search engine for all your test data. On 17 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿, Michael Rosam and Tomáš Neubauer will show how F1 organisations use this approach to get answers from their data faster. They’ll demonstrate how to connect recordings with the engineering context around them, search across large volumes of historical test data, and give AI agents enough structure to investigate a problem rather than simply retrieve files. Sign up to see how it works in practice 👇